Acta Psychiatr Scand 2018: 138: 558–570
All rights reserved
DOI: 10.1111/acps.12953
© 2018 The Authors Acta Psychiatrica Scandinavica Published by John Wiley & Sons Ltd
ACTA PSYCHIATRICA SCANDINAVICA
Social media and its relationship with mood,
self-esteem and paranoia in psychosis
Berry N, Emsley R, Lobban F, Bucci S. Social media and its
relationship with mood, self-esteem and paranoia in psychosis.
Objective: An evidence-base is emerging indicating detrimental and
beneficial effects of social media. Little is known about the impact of
social media use on people who experience psychosis.
Method: Forty-four participants with and without psychosis completed
1084 assessments of social media use, perceived social rank, mood, selfesteem and paranoia over a 6-day period using an experience sampling
method (ESM).
Results: Social media use predicted low mood, but did not predict selfesteem and paranoia. Posting about feelings and venting on social
media predicted low mood and self-esteem and high paranoia, whilst
posting about daily activities predicted increases in positive affect and
self-esteem and viewing social media newsfeeds predicted reductions in
negative affect and paranoia. Perceptions of low social rank when using
social media predicted low mood and self-esteem and high paranoia.
The impact of social media use did not differ between participants with
and without psychosis; although, experiencing psychosis moderated the
relationship between venting and negative affect. Social media use
frequency was lower in people with psychosis.
Conclusion: Findings show the potential detrimental impact of social
media use for people with and without psychosis. Despite few betweengroup differences, overall negative psychological consequences highlight
the need to consider use in clinical practice.
N. Berry1 , R. Emsley2 ,
F. Lobban3 , S. Bucci1,4
1
Division of Psychology and Mental Health, School of Health
Sciences, Faculty of Biology, Medicine and Health,
Manchester Academic Health Science Centre, University of
Manchester, Manchester, 2Department of Biostatistics and
Health Informatics, Institute of Psychiatry, Psychology
and Neuroscience, King’s College London, London,
3
Spectrum Centre for Mental Health Research, Lancaster
University, Lancaster, and 4Greater Manchester Mental
Health NHS Foundation Trust, Manchester, UK
This is an open access article under the terms of the
Creative Commons Attribution License, which permits
use, distribution and reproduction in any medium,
provided the original work is properly cited.
Key words: psychosis; schizophrenia; behaviour
Natalie Berry, Division of Psychology and Mental Health,
School of Health Sciences, University of Manchester,
Zochonis Building, Brunswick Street, M13 9PL
Manchester, UK.
E-mail: natalie.berry@manchester.ac.uk
Accepted for publication August 3, 2018
Significant outcomes
• This is the first study to use ESM to highlight the significant association between self-reported social media
•
•
engagement and subsequent reductions in mood at the next time-point in a sample of individuals with and without experience of psychosis.
Types of self-reported behaviours on social media were associated with subsequent changes in mood and paranoia at the next time-point. Specifically, posting about daily activities was associated with subsequent improvements in positive affect and self-esteem, whilst viewing social media newsfeeds led to significant reductions in
negative affect and paranoia and content consumption was associated with subsequent reduction in negative
affect. Conversely, posting about feelings and venting on social media were associated with subsequent reductions in mood and self-esteem and increases in paranoia and viewing profiles of individuals who were not
‘friends’ on social media and commenting on the posts/pictures of others led to increases in paranoia.
People with psychosis are significantly less likely to use social media than people without psychosis.
Limitations
• The sample was limited to mostly White British participants and clinicians may have been more likely to refer
•
•
558
patients who had previously expressed positive or negative experiences of engaging with social media, which
may limit the generalisability of the findings.
The design of the study was correlational in nature, so other factors may have also contributed towards the
impact of social media use.
The study was reliant on participants self-reporting social media use, which may be subject to inaccurate recall.
However, the use of ESM for self-reported use over a short time-scale is likely to have helped to mitigate this
limitation.
Introduction
The use of social media websites such as Twitter,
Facebook and Instagram is widespread. Social
media websites allow individuals to construct profiles in which they can maintain and create social
networks, circulate details about their daily lives
and respond to posts written by others (1). Rates
of social media use by people with severe mental
health problems such as psychosis are lower than
the general population based on small-scale studies
(2, 3).
People with mental health problems already use
social media websites to self-manage their mental
health. For example, social media can be a helpful
coping mechanism to facilitate self-expression and
communication with others with similar experiences and to access motivational content (4–6).
Clinicians have observed occasions where online
communication has been beneficial for clients’ with
SMI through accessible peer support and ability
for anonymous self-expression (7). Individuals are
amenable to the idea of receiving mental health
support via social media websites (8) and have suggested the inclusion of social media components,
such as moderated discussion forums, in future
interventions (9).
Despite some evidence for the potential therapeutic benefits of social media use, social media
engagement may also be harmful for an individual’s mental health and wellbeing. For example,
several studies have reported a significant link
between high social media use and low mood and
depression (10–12). However, others have found
no evidence of a link between social media use and
mood (13, 14). Mixed findings have also been
reported for the relationship between social media
use and self-esteem (15–18). Systematic reviews
have highlighted that conclusions cannot yet be
drawn and further robust research is warranted
(19, 20). It has been proposed that people with psychosis may be particularly vulnerable to paranoid
ideas after using social media websites (21), and
evidence from case reports suggests the development and exacerbation of symptoms associated
with severe mental health problems after social
media engagement (22–25). Individuals with psychosis may be more affected by content consumption on social media in comparison with those
without psychosis due to the posts written by
others often being open to individual interpretation. Specifically, people with psychosis can have
cognitive biases that can lead them to misinterpret
the actions and behaviours of others as threatening
or self-referent. Therefore, a virtual world where
one is continuously observing the content written
by others may facilitate individuals with psychosis
to observe and become suspicious by others
actions online. However, much of the current
research has relied on participants with severe
mental health problems retrospectively self-reporting whether they feel their use of social media leads
to paranoia (3, 22, 25). More recently, Bird et al.
(26) reported that the experience of negative affect
during social media use correlates with paranoia
severity. The lack of robust study designs and larger-scale research prevents conclusions from being
drawn.
Recent findings suggest there may be specific
aspects of social media use that determine whether
it is beneficial or detrimental. One such explanation is that social media may elicit downward
online social comparisons; that is, comparing oneself less favourably to others, leading to negative
feelings (27). Festinger (28) highlighted the importance of social comparison to explain the inherent
drive for individuals to achieve accurate self-evaluation of opinions and abilities. Social comparisons lead to the development of social ranks
(SRs), whereby individuals compare themselves to
others on relative power and social attractiveness
(29). Social media may facilitate the formation of
SRs due to the tendency for people to present
themselves and their experiences in a positive light
(30, 31). Researchers working in the field of social
comparisons and psychopathology have proposed
that perceived SR is associated with mood and
self-esteem (32, 33) and it has previously been
reported that negative social comparisons on social
media websites are associated with depression and
low self-esteem (34–37). Indeed, a recent editorial
proposed that individuals who experience mental
health problems may be more likely to be affected
by the social comparisons that social media elicits
due to a negative cognitive bias (38).
Researchers have now begun to explore whether
social media behaviours contribute to psychological outcomes. Burke et al. (39) separated social
media behaviours into (i) directed communication
such as posting on another user’s profile; (ii) content production such as posting status updates;
and (iii) content consumption such as scrolling
through social media newsfeeds. Additionally, a
recent review highlighted that active social media
use can enhance subjective wellbeing, whereas passive social media use reduces subjective wellbeing
(40). Therefore, consequences of social media use
may relate to social media activities, rather than
levels of social media use per se.
In order to examine the real-time relationship
between social media use and mental health and
wellbeing, some researchers have employed
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Social media use in psychosis
experience sampling methodology. For example,
Kross et al. (41) used ESM to demonstrate that
Facebook use predicted subsequent declines in
subjective wellbeing; although, this effect was not
predicted by direct communication on Facebook.
Further research used ESM to explore the relationship between passive Facebook use and wellbeing,
reporting that envy mediated the relationship
between passive Facebook use and declines in
affective wellbeing (42). Finally, Jelenchick et al.
(14) demonstrated no association between social
media use and depression in adolescents through
ESM data collection.
Aims
Previous research has mainly employed retrospective accounts of social media use, SR, mood, selfesteem and paranoia, with many studies conducted
in adolescent non-clinical samples. This study
aimed to explore in real time the impact of social
media use on mood, self-esteem and paranoia.
Specifically, we hypothesised:
H1: Social media use will predict low mood and
self-esteem and high paranoia.
H2: Passive social media use (content
consumption) will predict low mood and selfesteem and high paranoia, whilst active social
media use (content posting and direct
communication) will predict high mood and
self-esteem and low paranoia.
H3: Higher perceived SR in comparison with
others on social media will predict high mood
and self-esteem and low paranoia.
H4: The impact of self-reported social media
use and behaviours on mood, self-esteem and
paranoia will be moderated by a diagnosis of
psychosis, with a stronger relationship evident
in the clinical group.
Method
Design
Experience sampling method was used to capture momentary assessments of social media
use, mood, self-esteem and paranoia (withinsubjects), and paper-based questionnaires were
used to capture retrospective reports of these
variables (between-subjects). Experience sampling methodology (ESM) was chosen to
explore the impact of social media use because
it involves the repeated assessment of variables
over a specified time-period, which is more
ecologically valid than traditional retrospective
measures (43).
560
Participants
Participants were recruited via National Health
Service (NHS) secondary care mental health services in the UK and through promoting the
study on research volunteering websites. Clinical
participants were eligible to participate if they
had a clinician-verified experience of first episode psychosis or had received a diagnosis of
DSM-IV schizophrenia-spectrum disorder or
bipolar disorder. Non-clinical participants were
eligible to participate if they self-reported no
current experiences of mental health problems.
Further eligibility criteria for all participants
were as follows: (i) 18 years of age or over; (ii)
able to speak and read English; (iii) able to
provide informed consent; (iv) available for a
week-long study; (v) self-report Facebook or
Twitter account; and (vi) self-report social
media use at least three times per week. This
paper reports findings from the non-clinical and
psychosis participants only (n = 44) due to the
small number of participants in the sample with
bipolar disorder (n = 6). Findings including participants experiencing bipolar disorder are available from the author on request.
Baseline and trait-level measures
Trait-level mood was measured using the Positive
and Negative Affect Schedule (44). The PANAS
consists of 20 adjectives associated with positive
affect (PA) and negative affect (NA) and respondents are asked to indicate their levels of agreement on a 5-point Likert scale (1 = very slightly or
not at all; 5 = extremely). The scale can be used to
measure both trait- and state-levels of PA and NA
depending on question phrasing (44). The scale has
excellent internal consistency for both PA
(a = 0.86–0.90) and NA (a = 0.84–0.87). In this
study, Cronbach’s alpha was 0.90 for PA and 0.91
for NA.
Trait self-esteem was measured using the Rosenberg Self-esteem Scale (RSES), which consists of
10 statements on a 4-point scale (1 = strongly
agree; 4 = strongly disagree) and can be used to
measure both trait and state self-esteem (45). The
scale has also previously shown excellent internal
consistency and test–retest reliability (r = 0.85–
0.88) (46). In this study, the Cronbach’s alpha was
0.95.
Trait paranoia was measured using the Paranoia
Scale, which comprises 20 statements on a 5-point
Likert scale (1 = not at all applicable to me;
5 = extremely applicable to me). The scale has
been used in both clinical and non-clinical samples
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Berry et al.
and has shown good construct validity (a = 0.84)
and test–retest reliability (r = 0.70) (47) and
demonstrated excellent internal consistency in this
study (a = 0.95).
Baseline perceptions of SR were measured using
the Social Comparison Scale (SCS, which consists
of a list of 11 pairs of antonyms) (48). Participants
are asked to indicate on a 10-point scale how they
feel in comparison with others (e.g. 1 = inferior,
10 = superior). The higher the SCS score, the
higher the perceived SR relative to others. The
SCS has demonstrated good internal consistency
in both clinical (a = 0.91) and non-clinical
(a = 0.88) samples (48) and showed good internal
consistency in this study (a = 0.83).
Baseline assessments of social media use were
produced after a review of the literature to identify
items that had been used in previous studies. The
Social Media Use Integration Scale (SMUIS) was
included to measure participants’ emotional connection to social media and integration into their
daily lives (48). The SMUIS is a 10-item scale ranging from 0 (strongly disagree) to 5 (strongly agree)
and has shown strong internal consistency
(a = 0.91) and good test–retest reliability
(r = 0.80) and can be modified for other social
media platforms (49). The subsequent six questions
focussed on social media privacy settings and were
taken from research published by Ross et al. (50).
A further 14 questions were adapted from the same
questionnaire to assess how often participants
reported engagement in certain social media behaviours (eight response options, range: ‘never’ to
‘more than once a day’) and assigned to the overarching activities of content posting and direct communication (active social media use) and content
consumption (passive social media use). Social
media behaviours were assigned to these overarching categories based on the criteria published by
Burke et al. (39). Specifically, the activities of writing status updates (including status updates about
daily activities, feelings, opinions and venting) and
posting pictures/videos were assigned as content
posting; social media newsfeed/timeline scrolls and
viewing friends or strangers profiles were assigned as
content consumption; commenting on another person’s post/picture, liking another person’s post/picture and sharing another person’s post/picture were
assigned as direct communication. These activities
were defined and agreed during the development of
the ESM items and prior to data collection.
State-level (ESM) measures
The first question of the ESM assessments asked
participants whether they had used social media
since the last assessment. If participants selected
‘yes’, they were asked additional sets of questions
regarding social media use prior to completing the
remaining ESM assessments: (i) social media website used; (ii) social media activities; and (iii) feelings in comparison with others on social media
using the SCS (44). The responses from question 2
were characterised under content posting, direct
communication and content consumption also
using the criteria developed by Burke et al. (39).
Responses for question 3 were combined to produce the SCS scores for each participant at each
time-point. In this study, the momentary assessment of SR using the SCS had a Cronbach’s alpha
of 0.96.
To account for the potential that other forms of
personal interaction may lead to changes in the
variables of interest, participants were asked
whether they had spoken with another person since
the last assessment (yes/no) and to indicate
whether this communication was face-to-face,
online, text-message, telephone or a messaging
smartphone application. Participants were presented with nine mood-related adjectives on a 7point Likert scale (51) and asked the degree to
which the adjective described their current feelings
(1 = not at all; 7 = very). Responses to negative
and positive adjectives were combined for each
participant to give a total value for NA and PA at
each time-point and demonstrated excellent internal consistency (a = 0.93; a = 0.94 respectively).
The subsequent two questions used four ESM selfesteem items and four paranoia items (51–53). For
both questions, participants were asked to indicate
on a 7-point Likert scale the extent to which they
agreed or disagreed with the items (1 = not at all;
7 = very). Both the state self-esteem scale
(a = 0.96) and state paranoia scale (a = 0.92)
demonstrated excellent internal consistency in this
study.
A full list of ESM items is available in
Appendix S1.
Procedure
Ethical approval was granted by the local research
ethics committee. After consent, participants completed a demographics questionnaire and the baseline and trait-level measures in order to provide a
description of the sample. Participants were given
a unique username and password to access the
ESM assessments and completed a trial run either
on their own smartphone or a smartphone loaned
to them for the duration of the study. Participants
who were loaned a smartphone for the study were
asked to only use the smartphone for activities
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Social media use in psychosis
associated with the study. Specifically, participants
were asked to only use the smartphones to contact
the researcher with any technical questions that
arose during the study period and to complete the
momentary assessments. ESM assessments commenced the following morning. Text-messages containing a link to a secure online site were sent to
participants at six pseudo-random times a day over
a 6-day period. Participants had up to 15 min to
click the link and complete the assessments after
each text-message received. The data collection
period ranged from 10:00 and 21:00; although, this
could be adapted to allow for individual differences in waking hours. Participants were asked to
complete an exit evaluation detailing reasons for
missed assessments, debriefed by the researcher
about the study and provided £20 vouchers (not
contingent on the number of assessments completed).
Data analysis
Trait-level mood, self-esteem, paranoia and perceived SR were analysed by comparing betweengroup means using independent t-tests in SPSS Version 22 (IBM Corp., Armonk, NY, USA). ESM
data were assessed for normality through visual
inspection of histograms and analysis of skewness
and kurtosis. Analyses of ESM data were performed using Stata, version 14 (StataCorp, College
Station, TX, USA). The hierarchical structure of
ESM data (observations are nested within days,
within participants) requires multilevel modelling
to be used due to the violation of the assumption
of independence of observations. Using maximum
likelihood, we fitted 3-level random intercept models a random intercept for each participant, a random intercept for each participant-day and
participant-beep error term.
To test whether self-reported social media use
(H1), social media behaviours (H2) and perceived
SR (H3) predicted mood, self-esteem and paranoia at the next time-point, separate multilevel
linear regression analyses were estimated with
PA, NA, self-esteem and paranoia as the outcome variables, and self-reported social media
use, behaviours and perceived SR as the predictor
variables. In all models, socialisation (whether or
not an individual had spoken with another person) and group (clinical and non-clinical) were
included as covariates to ensure any associations
with the outcome variables could be attributed to
the use of social media, rather than other forms
of socialisation or group. To investigate whether
the impact of social media use was moderated by
a diagnosis of psychosis (H4), a further set of
562
multilevel linear regressions were estimated by
looking at the two-way interaction between group
and social media use, with employment status
and socialisation included as covariates. Finally,
whilst we did not make any specific hypotheses
relating to between-group differences in social
media use, odds ratios (ORs) and the corresponding 95% confidence intervals were calculated
through a multilevel logistic regression to compare the likelihood of social media use between
the clinical and non-clinical groups.
Results
Sample characteristics
Fifty-one people (26 clinical and 25 non-clinical)
consented to participate. Data provided from
one clinical participant were excluded from analyses as they did not complete any assessments
over the study period. Data from participants
with bipolar disorder (n = 6) were excluded due
to the small sample size. Therefore, the data
from a total of 44 participants (25 non-clinical;
19 psychosis) are reported in this paper. Demographic information is presented in Table 1. The
mean age of participants in the psychosis
(M = 33.7, SD = 9.7, range = 22–54) and nonclinical (M = 35.4, SD = 14.7, range = 20–62)
groups did not significantly differ (t(42) = 0.45,
P = 0.655). Three participants (16%) in the clinical group and one participant (4%) in the nonclinical group borrowed a smartphone for the
duration of the study. All borrowed smartphones were returned undamaged. Two participants in the clinical group and one participant
in the non-clinical group borrowed a smartphone because they did not own a smartphone;
one participant in the clinical group borrowed a
smartphone because their own smartphone had
poor data connectivity.
Completion rates
Out of the total 1584 assessments that were possible during the study period, 1084 were fully completed by participants (n = 458 clinical; n = 626
non-clinical; 68.4% response rate). The proportion
of assessments completed per participant ranged
between 25% and 91.6%. The mean number of
completed assessments did not significantly differ between clinical and non-clinical groups
[t(42) = 0.63, P = 0.532], males and females
[t(42) = 1.00, P = 0.322], on the basis of whether
a participant borrowed or owned the smartphone
that was used for data entry [t(42) = 0.33,
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Berry et al.
Table 1. Participant demographic information, clinical characteristics and social media use
Clinical group
Gender
Male
Female
Employment status
Working full time
Working part time
Working voluntary
Student
Unemployed
Ethnicity
Asian or Asian British
White British
White Other
Mixed Race
Highest level of education
High school
College
Some University
Undergraduate degree
Postgraduate degree
Diagnosis
First episode psychosis
Schizophrenia
Schizoaffective disorder
Paranoid schizophrenia
Psychosis NOS
Social media websites currently used
Facebook
Twitter
Instagram
Google+
Tumblr
Flickr
Social media apps currently used
Facebook
Twitter
Instagram
WhatsApp
Snapchat
Google+
Devices used to access social media
Laptop
Desktop
Smartphone
Tablet
n (%)
7 (37)
12 (63)
0 (0)
2 (11)
2 (11)
2 (11)
13 (68)
1 (4)
18 (95)
0 (0)
0 (0)
5 (26)
9 (47)
3 (16)
2 (11)
0 (0)
9 (47)
3 (16)
4 (21)
2 (11)
1 (5)
19 (100)
91 (47)
9 (47)
10 (53)
3 (16)
2 (11)
17 (89)
9 (47)
8 (42)
11 (58)
6 (32)
7 (37)
8 (42)
5 (26)
18 (95)
8 (42)
Non-clinical group
Gender
Male
Female
Employment status
Working full time
Working part time
Working voluntary
Student
Unemployed
Ethnicity
Asian or Asian British
White British
White other
Mixed Race
Highest level of education
High school
College
Some University
Undergraduate degree
Postgraduate degree
n (%)
11 (44)
14 (56)
v2 = 0.23, df = 1
13 (52)
3 (12)
0 (0)
7 (28)
2 (8)
v2 = 25.70, df = 4
1 (4)
21 (84)
2 (8)
1 (4)
4 (16)
4 (16)
4 (16)
10 (40)
3 (12)
–
–
–
–
–
Social media websites currently used
Facebook
25 (100)
Twitter
15 (60)
Instagram
10 (40)
Google+
6 (24)
Tumblr
1 (4)
Flickr
1 (4)
Social media apps currently used
Facebook
23 (92)
Twitter
13 (52)
Instagram
11 (44)
WhatsApp
18 (72)
Snapchat
12 (48)
Google+
5 (20)
Devices used to access social media
Laptop
17 (68)
Desktop
10 (40)
Smartphone
23 (92)
Tablet
6 (24)
P = 0.745], employment status (F(4, 39) = 0.86,
P = 0.495) or level of education (F(4, 39) = 0.22,
P = 0.928). Finally, there was no correlation
between assessment completion rate and age
(r = 0.64, P = 0.678).
It is generally accepted in ESM studies that participants should complete at least a third of assessments for the data to be included in analyses (54).
However, this value is arbitrary and given that
ESM is not affected by issues relating to missing
data, a specific completion rate of assessments is
not required (55). One person in the study completed less than the specified value (25%), but the
data they provided were still usable for the purpose
of this study due to the variations in the timing of
P
Test statistic
Total n (%)
0.632
18 (41)
26 (59)
<0.001
13 (30)
5 (11)
2 (5)
9 (21)
15 (30)
v2 = 2.46, df = 3
0.483
2 (5)
39 (89)
2 (5)
1 (2)
v2 = 9.88, df = 4
0.043
9 (21)
13 (30)
7 (16)
12 (27)
3 (7)
1.000
0.405
0.625
0.051
0.178
0.395
44 (100)
24 (55)
19 (43)
16 (37)
4 (9)
3 (7)
–
–
–
–
–
–
–
–
–
–
v2
v2
v2
v2
v2
=
=
=
=
=
0.70, df = 1
0.24, df = 1
3.82, df = 1
1.82, df = 1
0.72 df = 1
v2
v2
v2
v2
v2
v2
=
=
=
=
=
=
0.83, df
0.93, df
0.16, df
0.96, df
1.20, df
1.54, df
=
=
=
=
=
=
1
1
1
1
1
1
0.773
0.761
0.900
0.328
0.272
0.214
40 (91)
22 (50)
19 (43)
29 (66)
18 (41)
12 (27)
v2
v2
v2
v2
=
=
=
=
2.95, df
0.90, df
0.13, df
1.63, df
=
=
=
=
1
1
1
1
0.086
0.343
0.721
0.202
25 (57)
15 (34)
41 (93)
14 (32)
the responses and was, therefore, included in all
analyses.
Demographic characteristics and social media use
General social media use across the study duration
was not significantly related to participant age
(r = 0.24, P = 0.116), but was associated with
mental health status, with participants in the clinical group reporting lower levels of social media use
in comparison with participants in the non-clinical
group [t(42) = 2.15, P = 0.037]. Social media use
was not significantly associated with gender [t
(42) = 1.41, P = 0.166], whether a participant
borrowed or owned the smartphone that was used
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Social media use in psychosis
for data entry [t(42) = 1.17, P = 0.249], employment status (F(4, 39) = 2.00, P = 0.114) or level of
education (F(4, 42) = 0.64, P = 0.638).
Between-group differences in trait measures
Trait-level self-esteem, perceived SR, mood and
paranoia are presented in Table 2. Participants in
the non-clinical group had significantly higher
scores for trait-level self-esteem and PA in comparison with the clinical group, whilst participants in
the clinical group had significantly higher scores
for trait-level NA and paranoia. There were no significant between-group differences in trait-level SR
scores.
Does social media use predict subsequent mood, self-esteem and
paranoia?
Table 3 demonstrates that H1 was partially supported. Social media use negatively predicted PA
and positively predicted NA. However, social
media use did not predict self-esteem or paranoia.
Multilevel logistic regression analyses were conducted to explore whether trait variables predicted
social media use across the study period. Traitlevel PA (b = 0.0021, SE = 0.0190, p = 0.913,
95% CI [ 0.035 to 0.039]), NA (b = 0.0079,
SE = 0.0173, P = 0.646, 95% CI [ 0.042 to
0.026]), self-esteem (b = 0.0018, SE = 0.0226,
P = 0.938, 95% CI [ 0.042 to 0.046]), paranoia
(b = 0.0127, SE = 0.0074, P = 0.085, 95% CI
[ 0.027 to 0.002]) and SR (b = 0.0096,
SE = 0.0115, P = 0.403, 95% CI [ 0.032 to
0.013]) did not predict social media use. Therefore,
it likely that social media use predicted mood,
rather than being predicted by mood.
Do reported social media behaviours predict subsequent mood,
self-esteem, paranoia and perceived SR?
Table 4 shows that content posting did not predict
PA, self-esteem or perceived SR, but did positively
predict NA and paranoia. Content consumption
and direct communication were not found to
predict PA, self-esteem, paranoia or perceived SR.
However, content consumption did significantly
negatively predict NA. Therefore, H2 was not
supported.
Posting about daily activities led to increases in
PA and self-esteem at the next time-point. Conversely, posting about feelings led to subsequent
increases in NA and paranoia and reductions in
PA, self-esteem and perceived SR; venting on
social media negatively predicted PA and selfesteem and positively predicted NA and paranoia;
looking through social media newsfeeds negatively
predicted NA and paranoia; viewing a ‘friends’
social media profile positively predicted perceived
SR; viewing profiles of people who were not
‘friends’ on social media positively predicted paranoia; and commenting on other peoples’ statuses
positively predicted paranoia.
Does perceived SR when using social media predict subsequent
mood, self-esteem and paranoia?
Table 5 shows that higher perceived SR when
using social media positively predicted PA and
self-esteem and negatively predicted NA and paranoia. These findings support H3 and demonstrate
that perceptions of high SR when using social
media predict subsequent increases in mood and
self-esteem and decreases in paranoia.
Are lower scores for positive affect and higher scores for negative
affect after social media use moderated by experiencing
psychosis?
Psychosis was not found to moderate the relationship between social media use and PA (b = 0.1974,
SE = 0.4969, P = 0.691, 95% CI [ 0.776 to 1.171])
or NA (b = 0.0876, SE = 0.5291, P = 0.869,
95% CI [ 1.125 to 0.949]). This finding does not
support H4 that psychosis would moderate associations between social media use and mood.
We also explored whether psychosis moderated
the relationship between specific types of social
media use that had led to significant changes in
outcome variables. Psychosis did not moderate the
Table 2. Participant trait-level scores of self-esteem, perceived social rank, positive affect, negative affect and paranoia
Clinical group
n
M
SD
Non-clinical group
n
M
SD
Test statistic
Self-esteem
Social rank
Positive affect
Negative affect
Paranoia
25
25
25
25
25
12.6
54.2
29.5
25.6
58.6
6.1
16.6
9.5
8.3
20.3
Self-esteem
Social rank
Positive affect
Negative affect
Paranoia
24*
25
25
25
25
22.3
61.3
36.7
17.7
32.4
3.5
9.6
4.9
7.6
10.3
t (41)
t (42)
t (42)
t (42)
t (42)
*Missing data from 1 participant.
564
= 6.5
= 1.8
= 3.2
= 3.3
= 5.6
P
<0.001
0.079
0.002
0.002
<0.001
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Berry et al.
Table 3. Effect of social media use on positive and negative affect, self-esteem
and paranoia. Effect is unstandardised b coefficient from separate models
Table 5. Effect of perceived social rank on positive affect, self-esteem, negative
affect and paranoia. Effect is unstandardised b coefficient
Dependent
variable
Effect
Dependent
variable
Effect
Positive affect
Negative affect
Self-esteem
Paranoia
0.5027
0.5593
0.0498
0.1272
Positive affect
Self-esteem
Negative affect
Paranoia
0.1232
0.0901
0.1186
0.0780
Standard error
P
0.2469
0.2628
0.1783
0.1794
0.042
0.033
0.780
0.478
95% confidence interval
0.9866, 0.0188
0.0442, 1.0744
0.2996, 0.3993
0.2245, 0.4789
relationship between: (i) content posting and NA
(b = 0.7558, SE =0.9746, P = 0.438, CI [ 1.154 to
2.666]) or paranoia (b = 0.0882, SE = 0.6580,
P = 0.894, CI [ 1.380 to 1.203]); (ii) posting
about daily activities and PA (b = 0.7114,
SE = 1.0824, P = 0.511, CI [ 1.410 to 2.833]) or
self-esteem (b = 1.1927, SE = 0.7537, P = 0.114,
CI [ 0.284 to 2.670]); (iii) posting about feelings
and PA (b = 1.0790, SE = 1.7409, P = 0.535,
CI [ 4.491 to 2.333]), NA (b = 3.2388,
SE = 1.8877, P = 0.086, CI [ 0.461 to 6.939]),
self-esteem
(b = 0.7087,
SE = 1.2316,
P = 0.565, CI [ 3.123 to 1.705]), paranoia
(b = 1.1283, SE = 1.2722, P = 0.375, CI [ 3.622
to 1.365]) or perceived SR (b = 3.1986,
SE = 4.7332, P = 0.499, CI [ 6.078 to 12.476]);
(iv) venting on social media and PA (b = 2.3189,
SE = 2.1563, P = 0.282, CI [ 6.545 to 1.907]),
self-esteem (b = 0.4254, SE = 1.5018, P = 0.777,
CI [ 2.518 to 3.369]) or paranoia (b = 0.6628,
SE = 1.5572, P = 0.670, CI [ 3.715 to 2.389]); (v)
content consumption and NA (b = 1.2609,
SE = 1.0988, P = 0.251, CI [ 3.414 to 0.893]);
(vi) viewing social media newsfeeds and NA
(b = 0.4351, SE = 1.0026, P = 0.664, CI [ 1.530
Standard error
P
0.0132
0.0098
0.0152
0.0105
<0.001
<0.001
<0.001
<0.001
95% confidence interval
0.0976, 0.1488
0.0710, 0.1092
0.1484, 0.0888
0.0987, 0.0574
to 2.40]) or paranoia (b = 0.0764, SE = 0.6698,
P = 0.909, CI [ 1.389 to 1.236]); (vii) viewing
profiles of friends and perceived SR (b = 0.8748,
SE = 2.3684, P = 0.714, CI [ 3.802 to 5.552]);
vii) viewing profiles of strangers and paranoia
(b = 1.3931, SE = 0.8995, P = 0.121, CI [ 3.156
to 0.370]); and (viii) commenting on another person’s post or picture (b = 0.7896, SE = 0.6675,
P = 0.237, CI [ 0.519 to 2.098]. However, the
relationship between venting on social media and
subsequent NA was significantly moderated by
psychosis (b = 4.7100, SE = 2.3101, P = 0.041,
CI [0.182–9.238]).
Do social media use and behaviours differ between people with
and without psychosis?
Clinical participants were less likely to use social
media
than
non-clinical
participants
(OR = 0.5366, SE = 0.1550, P = 0.031, 95% CI
[0.305–0.945]). Separate analyses revealed that
clinical participants were less likely to use Facebook (OR = 0.5394, SE = 0.1692, P = 0.049, CI
[0.292–0.998]), but there were no differences in
Table 4. Effect of reported social media behaviours on positive and negative affect, self-esteem, paranoia and perceived social rank
Positive affect
n
Content posting
Posting about daily activities
Posting about opinions
Posting about feelings
Posting pictures/videos of self or others
Venting on social media
Content consumption
Looking through newsfeed
Viewing friends’ profiles
Viewing profiles of people who
are not friends
Direct communication
Commented on another person’s
post/picture
Liked another person’s post/picture
Shared another person’s post/picture
b
SE
Negative affect
b
SE
Self-esteem
Β
Paranoia
SE
b
Perceived social rank
SE
b
SE
114
71
20
22
17
13
514
470
121
47
0.1148
1.3171*
1.082
4.8410***
0.4534
4.4309***
0.1816
0.2280
0.5025
0.7552
0.4468
0.5390
0.8993
0.8555
0.9413
1.0676
0.4710
0.4444
0.4346
0.6107
1.2316*
0.7508
0.0392
4.3416 ***
0.3502
4.3563***
1.1180*
1.0748*
0.0271
0.6353
0.4801
0.5904
0.9713
0.9362
1.0063
1.1480
0.4993
0.4720
0.4672
0.6549
0.0499
0.9967**
0.2772
1.6865**
0.1546
1.7770*
0.5389
0.5788
0.1166
0.4298
0.3108
0.3760
0.6235
0.6066
0.6485
0.7426
0.3251
0.3078
0.3012
0.4219
0.6638*
0.1629
0.1005
2.7189 ***
0.3190
2.1293**
0.5269
0.8823**
0.1016
1.2631**
0.3237
0.3987
0.6497
0.6279
0.6727
0.7697
0.3346
0.3148
0.3133
0.4349
1.1846
0.7363
1.6827
7.9489**
2.3035
2.8936
1.7166
1.9190
2.7343*
2.9803
1.1983
1.4666
2.4000
2.2059
2.4867
2.8688
1.2481
1.1773
1.1505
1.6407
166
113
0.0148
0.0416
0.3787
0.4315
0.1451
0.0571
0.4069
0.4649
0.0255
0.0511
0.2624
0.2993
0.4852
0.6584*
0.2716
0.3102
0.9634
1.0097
1.0078
1.1515
43
32
0.1700
0.2047
0.7014
0.7112
0.2100
0.1488
0.7531
0.7612
0.0748
0.0489
0.4871
0.4900
0.0126
0.2866
0.5041
0.5099
1.0298
1.9830
1.8664
1.8829
***P < 0.001, **P < 0.01, *P < 0.05.
565
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Social media use in psychosis
Twitter (OR = 1.1484, SE=0.9881, P = 0.871,
95%
CI
[0.213–6.201])
or
Instagram
(OR = 0.6580, SE=0.7151, P = 0.700, 95% CI
[0.078–5.536]) use.
Discussion
This study aimed to identify whether social media
use predicted subsequent mood, self-esteem and
paranoia, pinpointing any specific social media
behaviours reported by participants that contributed towards relationships observed. Additionally, we aimed to determine whether perceptions of
SR when using social media predicted these outcomes and to ascertain whether experiencing psychosis moderated any relationship between social
media use and mood, self-esteem and paranoia.
As hypothesised, social media use predicted
reductions in PA and elevations in NA. However,
contrary to expectations, social media use was not
found to predict self-esteem or paranoia. Further
analyses revealed that posting about daily activities
predicted high PA and self-esteem, whereas posting about feelings and venting on social media predicted low PA, self-esteem and perceived SR and
high NA and paranoia. Seidman (2013) proposed
that posting about feelings and venting on social
media is an emotional form of self-disclosure,
whereas posting information about daily activities
is general self-disclosure (56). Emotional self-disclosures are important for social connectedness,
belonging and feelings of intimacy (57–59) and
people perceive personal disclosures on social
media as a helpful mechanism to connect with
others, maintain relationships, exchange opinions
and receive support (60–62). However, in contrast
to these findings, it was general factual-based disclosures that were beneficial for increasing mood,
whilst emotional-based posting was detrimental to
mood and paranoia. One tentative explanation for
this unexpected finding is that participants did not
receive the supportive and reinforcing responses
they hoped for when they posted emotional selfdisclosures. This possibility is supported by recent
research that found social attraction towards social
media users was lower when posts contained highly
personal or negative self-disclosures (63) and that
negative online disclosures by individuals with low
self-esteem receive undesirable responses due to
the negativity expressed (64).
In contrast with previous research (37, 40), content consumption was found to lead to decreases in
NA and there was no relationship between content
consumption and perceived SR whilst engaging
with social media. The discrepancy in findings
566
between this study and previous studies may be
linked to the recent changes in content that individuals observe on social media and, in particular,
Facebook. Specifically, it has been widely reported
that social media websites now contain a disproportionate number of advertisements and news
articles in comparison with actual status updates
and posts from social media friends. Indeed, a
recent announcement by Facebook in 2018 highlighted the evident reduction in the presence of personal posts on the platform due to the plethora of
posts from business and the media, resulting in the
aim to reduce this public content (65). Therefore,
social media consumption may have been less
likely to elicit comparative self-reflections than previous studies due to the reduced exposure to others
lives prevalent in the past. The evolving nature of
social media platforms means that future research
should examine the impact of the upcoming
change in website content.
Experiencing psychosis did not moderate the
impact of social media use or behaviours; however,
this did moderate the relationship between venting
and NA. This suggests that maladaptive coping
strategies, such as venting, may be more detrimental for individuals who experience psychosis in
comparison with the general population.
Although, on the whole, a diagnosis of psychosis
did not moderate the impact of social media use
and behaviours, the finding remains clinically
important because the impact of social media use
may have more serious consequences in individuals
with psychosis due to lower mood prior to engagement. Therefore, the observed impact of social
media use warrants further consideration in the
context of psychosis and mental health and wellbeing more generally. Additionally, clinical participants were significantly less likely to use social
media than non-clinical participants. Small-scale
studies demonstrate lower social media website
access by individuals with severe mental health
problems than the general population (2, 3); however, this is the first to identify differences in frequency of use. Higher levels of paranoia were also
associated with lower levels of social media use
and, given that the clinical group showed significantly higher scores for both trait- and state-level
paranoia, it is likely that the experience of paranoia led to lower levels of social media use. Other
potential reasons for these findings should be
explored in future studies to determine whether
differences in social media use frequency are due to
barriers to use or other factors such as symptom
occurrence or avoiding social media to prevent any
associated negative outcomes.
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Berry et al.
Strengths, limitations and directions for future research
A strength of the current study was the ecologically valid prospective measurement of social
media use and psychological outcomes. Additionally, socialisation was included to identify whether
other forms of social interaction may have predicted outcomes. Trait SR, mood, self-esteem and
paranoia were also measured at baseline and were
not found to predict social media use. Therefore,
although exact directions cannot be established,
the inclusion of these trait measures improves
confidence in conclusions regarding outcomes
associated with social media use. Comparison of
clinical and non-clinical groups allowed the
between-group comparison of the impact of social
media use.
We decided not to actively track participants’
social media use due to (i) the costs associated with
the passive monitoring of accounts; (ii) concerns
that seeking permission to view social media
accounts would affect recruitment rates; therefore,
negatively impacting the sample size; (iii) the
potential for risk identification on accounts and
the resulting responsibility for the researcher to
report such information; (iv) the potential for participants to change their behaviours on social
media if they are aware that a researcher is monitoring their profiles; (v) monitoring would only
allow the identification of content posting behaviours; not content consumption; and (vi) accessing participant social media profiles would enable
the identification of posts written by others on the
individual’s profile, who will not have provided
consent for researchers to view their posts. ESM
was used to reduce the likelihood of inaccurate retrospective recall due to the momentary nature of
alerts; however, monitoring participants’ social
media access and behaviours may have produced
more reliable data due to the self-reported nature
of the study. Additionally, we speculated that the
detrimental impact of emotional self-disclosures
may be related to the feedback individuals
received; however, without clear knowledge of user
responses to posts, firm conclusions cannot be
drawn. Therefore, future research should seek to
identify the responses to such disclosures to understand whether response type mediates the relationship between disclosure posting and outcome. It is
also possible that the information participants
were presented with when viewing social media
profiles and newsfeeds may have contributed
towards reductions in mood when using social
media. Participants were not asked to provide
details about the information they saw each time
they accessed social media sites. Future research
should expand on these findings by exploring
whether type of content viewed contributes
towards the impact of use and behaviours. The
sample was limited to mostly White British participants so findings are unlikely to be generalisable.
Moreover, clinicians may have been more likely to
refer patients who had previously described positive or negative experiences of engaging with social
media. The design of the study was correlational in
nature and it is likely that other factors may have
also contributed towards the impact of social
media use. Therefore, future research should seek
to experimentally manipulate social media use to
explore whether the effect is directly attributable
specifically to social media use and behaviours.
Finally, a diagnosis of SMI was not found to moderate the relationship between social media use and
mood; however, the sample size may have not been
sufficient for moderation. Therefore, future
research should explore the impact of an SMI
diagnosis with a larger pool of participants.
Clinical implications
Despite finding that psychosis did not moderate
the impact of social media use per se, reductions in
mood after social media use are likely to be more
damaging for people with psychosis due to reporting lower levels of mood initially. The negative
consequence of social media engagement in both
groups highlights the importance of continued
consideration of the impact of social media in mental health settings. Specifically, clinicians should
ensure that they are aware of and explore any
potential issues clients face when using social
media, particularly with regard to online self-disclosures. Additionally, the findings support the
recent assertion made by the Royal Society for
Public Health (2017) that social media websites
should be used to discretely reach out to individuals who may be affected by content and signpost
appropriate support options (66). Social networking components such as forums could be incorporated into digital health interventions for severe
mental health problems to connect people with
similar experiences and provide professional and
peer support (67–72). However, our findings suggest that emotional disclosures via these platforms
have the potential to elicit negative feelings. We
speculated that the impact of such disclosures may
be due to the absence of supportive feedback;
therefore, these social networking components
should be moderated to ensure individuals receive
supportive responses when self-disclosing personal
information. Finally, we identified significantly
lower frequencies of social media use by people
567
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Social media use in psychosis
who experience psychosis, which may be a potential barrier in the uptake of social networking components within DHIs. Further research is needed
to explore the reasons for this comparatively low
use and to identify whether these differences exist
on a larger-scale.
Acknowledgements
The authors would like to thank Mr Austin Lockwood for his
assistance in programming the survey used in this study and
the participants who took part. This study formed part of a
PhD studentship funded by the MRC Health eResearch Centre
(HeRC), Farr Institute, UK (MR/K006665/1).
Conflict of interest
The authors confirm that they have no competing interests to
declare.
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Supporting Information
Additional supporting information may be found online in the
Supporting Information section at the end of the article:
Appendix S1. ESM assessments.
16000447, 2018, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/acps.12953 by Middlesex University, Wiley Online Library on [03/11/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Berry et al.